External validation of the Bayesian Estimated Tools for Survival (BETS) models in patients with surgically treated skeletal metastases

Opportunistic CT for Prediction of Adverse Postoperative Events in Patients with Spinal Metastases

The purpose of this study was to assess the value of body composition measures obtained from opportunistic abdominal computed tomography (CT) in order to predict hospital length of stay (LOS), 30-day postoperative complications, and reoperations in patients undergoing surgery for spinal metastases. 196 patients underwent CT of the abdomen within three months of surgery for spinal metastases. Automated body composition segmentation and quantifications of the cross-sectional areas (CSA) of abdominal visceral and subcutaneous adipose tissue and abdominal skeletal muscle was performed. From this, 31% (61) of patients had postoperative complications within 30 days, and 16% (31) of patients underwent reoperation. Lower muscle CSA was associated with increased postoperative complications within 30 days (OR [95% CI] = 0.99 [0.98-0.99], p = 0.03). Through multivariate analysis, it was found that lower muscle CSA was also associated with an increased postoperative complication rate after controlling for the albumin, ASIA score, previous systemic therapy, and thoracic metastases (OR [95% CI] = 0.99 [0.98-0.99], p = 0.047). LOS and reoperations were not associated with any body composition measures. Low muscle mass may serve as a biomarker for the prediction of complications in patients with spinal metastases. The routine assessment of muscle mass on opportunistic CTs may help to predict outcomes in these patients.

Life Expectancy After Treatment of Metastatic Bone Disease: An International Trend Analysis

INTRODUCTION

The decision to treat metastatic bone disease (MBD) surgically depends in part on patient life expectancy. We are unaware of an international analysis of how life expectancy among these patients has changed over time. Therefore, we asked (1) how has the life expectancy for patients treated for MBD changed over time, and (2) which, if any, of the common primary cancer types are associated with longer survival after treatment of MBD?

METHODS

We reviewed data collected from 2000 to 2022 in an international MBD database, as well as data used for survival model validation. We included 3,353 adults who underwent surgery and/or radiation. No patients were excluded. Patients were grouped by treatment date into period 1 (2000 to 2009), period 2 (2010 to 2019), and period 3 (2020 to 2022). Cumulative survival was portrayed using Kaplan-Meier curves; log-rank tests were used to determine significance at P < 0.05. Subgroup analyses by primary cancer diagnosis were performed.

RESULTS

Median survival in period 2 was longer than in period 1 ( P < 0.001). Median survival (at which point 50% of patients survived) had not been reached for period 3. Median survival was longer in period 2 for all cancer types ( P < 0.001) except thyroid. Only lung cancer reached median survival in period 3, which was longer compared with periods 1 and 2 ( P < 0.001). Slow-growth, moderate-growth, and rapid-growth tumors all demonstrated longer median survival from period 1 to period 2; only rapid-growth tumors reached median survival for period 3, which was longer compared with periods 1 and 2 ( P < 0.001).

DISCUSSION

Median duration of survival after treatment of MBD has increased, which was a consistent finding in nearly all cancer types. Longer survival is likely attributable to improvements in both medical and surgical treatments. As life expectancy for patients with MBD increases, surgical methods should be selected with this in mind.

LEVEL OF EVIDENCE

VI.

Operative management of metastatic disease of the acetabulum: review of the literature and prevailing concepts

Metastatic disease of the periacetabular region is a common problem in orthopaedic oncology, associated with severe pain, decreased mobility, and substantial decline of the quality of life. Conservative management includes optimisation of pain management, activity modification, and radiation therapy. However, patients with destructive lesions affecting the weight-bearing portion of the acetabulum often require reconstructive surgery to decrease pain and restore mobility. The goal of surgery is to provide an immediately stable and durable construct, allowing immediate postoperative weight-bearing and maintaining functional independence for the remaining lifetime of the patient. A variety of surgical techniques have been reported, most of which are based upon cemented total hip arthroplasty, but also include porous tantalum implants and percutaneous cementoplasty. This review discusses the various reconstructive concepts and options, including their respective indications and outcome. A reconstructive algorithm incorporating different techniques and strategies based upon location and quality of remaining bone is also presented.

SORG algorithm to predict 3- and 12-month survival in metastatic spinal disease: a cross-sectional population-based retrospective study

PURPOSE

In this study, we wished to compare statistically the novel SORG algorithm in predicting survival in spine metastatic disease versus currently used methods.

METHODS

We recruited 40 patients with spinal metastatic disease who were operated at Geneva University Hospitals by the Neurosurgery or Orthopedic teams between the years of 2015 and 2020. We did an ROC analysis in order to determine the accuracy of the SORG ML algorithm and nomogram versus the Tokuhashi original and revised scores.

RESULTS

The analysis of data of our independent cohort shows a clear advantage in terms of predictive ability of the SORG ML algorithm and nomogram in comparison with the Tokuhashi scores. The SORG ML had an AUC of 0.87 for 90 days and 0.85 for 1 year. The SORG nomogram showed a predictive ability at 90 days and 1 year with AUCs of 0.87 and 0.76 respectively. These results showed excellent discriminative ability as compared with the Tokuhashi original score which achieved AUCs of 0.70 and 0.69 and the Tokuhashi revised score which had AUCs of 0.65 and 0.71 for 3 months and 1 year respectively.

CONCLUSION

The predictive ability of the SORG ML algorithm and nomogram was superior to currently used preoperative survival estimation scores for spinal metastatic disease.

Five decades of sarcoma care at Memorial Sloan Kettering Cancer Center

Early studies of the management of soft tissue sarcoma at Memorial Sloan Kettering Cancer Center were influenced by development of robust prospective long-term databases. Increasing capacity for molecular diagnostics has identified a myriad of subtypes with definable natural history. Accurate identification of tissue-specific risk of recurrence and disease-specific survival have increasingly allowed selective use of surgery, radiation therapy, and target-specific cytotoxic and immune therapies.

The Prediction of Survival after Surgical Management of Bone Metastases of the Extremities—A Comparison of Prognostic Models

Individualized survival prognostic models for symptomatic patients with appendicular metastatic bone disease are key to guiding clinical decision-making for the orthopedic surgeon. Several prognostic models have been developed in recent years; however, most orthopedic surgeons have not incorporated these models into routine practice. This is possibly due to uncertainty concerning their accuracy and the lack of comparison publications and recommendations. Our aim was to conduct a review and quality assessment of these models. A computerized literature search in MEDLINE, EMBASE and PubMed up to February 2022 was done, using keywords: “Bone metastasis”, “survival”, “extremity” and “prognosis”. We evaluated each model’s performance, assessing the estimated discriminative power and calibration accuracy for the analyzed patients. We included 11 studies out of the 1779 citations initially retrieved. The 11 studies included seven different models for estimating survival. Among externally validated survival prediction scores, PATHFx 3.0, 2013-SPRING and potentially Optimodel were found to be the best models in terms of performance. Currently, it is still a challenge to recommend any of the models as the standard for predicting survival for these patients. However, some models show better performance status and other quality characteristics. We recommend future, large, multicenter, prospective studies to compare between PATHfx 3.0, SPRING 2013 and OptiModel using the same external validation dataset.

Quantitative preoperative patient assessments are related to survival and procedure outcome for osseous metastases

Aims

Our objective was to determine if preoperative patient-reported assessments are associated with survival after surgery for stabilization of skeletal metastases.

Patients and Methods

All patients with metastatic cancer to bone and indications for skeletal stabilization surgery were approached to participate in a prospective cohort study at a tertiary care center from 2012 to 2017. Of the 208 patients who were eligible, 195 (94%) completed the 36-item Short Form Health Survey (SF-36) preoperatively and underwent surgical treatment of skeletal metastases with complete or impending fractures; the sample encompassed a range of cancer diagnoses and included cases of both internal fixation and endoprosthetic replacement. Cox proportional hazards models were used to identify associations between SF-36 scores and survival.

Results

In a model adjusted for clinical factors, patients' mental and physical SF-36 component summary scores were significantly associated with survival, as was their SF-36 composite score (P = 0.004, P = 0.015, and P < 0.001, respectively). Scores in the general health, vitality, and mental health domains were each strongly associated with survival (P < 0.001).

Conclusions

Patients' preoperative assessments of their health status are associated with their survival after surgery for skeletal metastases. Patient-reported assessments have the potential to contribute unique information to models that estimate patient survival, as part of efforts to provide optimal, individualized care and make informed decisions about the type and magnitude of surgery for metastatic bone disease that will last the patient's lifetime.

Machine learning algorithms to estimate 10-Year survival in patients with bone metastases due to prostate cancer: toward a disease-specific survival estimation tool

Background

Prognostic indicators, treatments, and survival estimates vary by cancer type. Therefore, disease-specific models are needed to estimate patient survival. Our primary aim was to develop models to estimate survival duration after treatment for skeletal-related events (SREs) (symptomatic bone metastasis, including impending or actual pathologic fractures) in men with metastatic bone disease due to prostate cancer. Such disease-specific models could be added to the PATHFx clinical-decision support tool, which is available worldwide, free of charge. Our secondary aim was to determine disease-specific factors that should be included in an international cancer registry.

Methods

We analyzed records of 438 men with metastatic prostate cancer who sustained SREs that required treatment with radiotherapy or surgery from 1989–2017. We developed and validated 6 models for 1-, 2-, 3-, 4-, 5-, and 10-year survival after treatment. Model performance was evaluated using calibration analysis, Brier scores, area under the receiver operator characteristic curve (AUC), and decision curve analysis to determine the models’ clinical utility. We characterized the magnitude and direction of model features.

Results

The models exhibited acceptable calibration, accuracy (Brier scores < 0.20), and classification ability (AUCs > 0.73). Decision curve analysis determined that all 6 models were suitable for clinical use. The order of feature importance was distinct for each model. In all models, 3 factors were positively associated with survival duration: younger age at metastasis diagnosis, proximal prostate-specific antigen (PSA) < 10 ng/mL, and slow-rising alkaline phosphatase velocity (APV).

Conclusions

We developed models that estimate survival duration in patients with metastatic bone disease due to prostate cancer. These models require external validation but should meanwhile be included in the PATHFx tool. PSA and APV data should be recorded in an international cancer registry.

Development of a scoring system for survival following surgery for metastatic bone disease

AIMS

The incidence of bone metastases is between 20% to 75% depending on the type of cancer. As treatment improves, the number of patients who need surgical intervention is increasing. Identifying patients with a shorter life expectancy would allow surgical intervention with more durable reconstructions to be targeted to those most likely to benefit. While previous scoring systems have focused on surgical and oncological factors, there is a need to consider comorbidities and the physiological state of the patient, as these will also affect outcome. The primary aim of this study was to create a scoring system to estimate survival time in patients with bony metastases and to determine which factors may adversely affect this.

METHODS

This was a retrospective study which included all patients who had presented for surgery with metastatic bone disease. The data collected included patient, surgical, and oncological variables. Univariable and multivariable analysis identified which factors were associated with a survival time of less than six months and less than one year. A model to predict survival based on these factors was developed using Cox regression.

RESULTS

A total of 164 patients were included with a median survival time of 1.6 years (interquartile range 0.5 to 3.1) after surgery. On multivariable analysis, a higher American Society of Anesthesiologists grade (p < 0.001), a high white cell count (p = 0.002), hyponatraemia (p = 0.001), a preoperative resting heart rate of > 100 bpm (p = 0.052), and the type of primary cancer (p = 0.026) remained significant predictors of reduced survival time. The predictive model developed showed good discrimination and calibration to predict both six- and 12-month survival in patients with metastatic bone disease.

CONCLUSION

In addition to surgical and oncological factors, the level of comorbidity and physiological state of the patient has a significant impact on survival in patients with metastatic bone disease. These factors should be considered when assessing the appropriateness of surgical intervention. This is the first study to examine other patient factors alongside surgical and oncological data to identify a relationship between these and survival. Cite this article: Bone Joint J 2021;103-B(11):1725-1730.

The preoperative machine learning algorithm for extremity metastatic disease can predict 90-day and 1-year survival: An external validation study

BACKGROUND

The prediction of survival is valuable to optimize treatment of metastatic long-bone disease. The Skeletal Oncology Research Group (SORG) machine-learning (ML) algorithm has been previously developed and internally validated. The purpose of this study was to determine if the SORG ML algorithm accurately predicts 90-day and 1-year survival in an external metastatic long-bone disease patient cohort.

METHODS

A retrospective review of 264 patients who underwent surgery for long-bone metastases between 2003 and 2019 was performed. Variables used in the stochastic gradient boosting SORG algorithm were age, sex, primary tumor type, visceral/brain metastases, systemic therapy, and 10 preoperative laboratory values. Model performance was calculated by discrimination, calibration, and overall performance.

RESULTS

The SORG ML algorithms retained good discriminative ability (area under the cure [AUC]: 0.83; 95% confidence interval [CI]: 0.76-0.88 for 90-day mortality and AUC: 0.84; 95% CI: 0.79-0.88 for 1-year mortality), calibration, overall performance, and decision curve analysis.

CONCLUSION

The previously developed ML algorithms demonstrated good performance in the current study, thereby providing external validation. The models were incorporated into an accessible application (https://sorg-apps.shinyapps.io/extremitymetssurvival/) that may be freely utilized by clinicians in helping predict survival for individual patients and assist in informative decision-making discussion before operative management of long bone metastatic lesions.

Current Overview of Treatment for Metastatic Bone Disease

The number of patients with bone metastasis increases as medical management and surgery improve the overall survival of patients with cancer. Bone metastasis can cause skeletal complications, including bone pain, pathological fractures, spinal cord or nerve root compression, and hypercalcemia. Before initiation of treatment for bone metastasis, it is important to exclude primary bone malignancy, which would require a completely different therapeutic approach. It is essential to select surgical methods considering the patient’s prognosis, quality of life, postoperative function, and risk of postoperative complications. Therefore, bone metastasis treatment requires a multidisciplinary team approach, including radiologists, oncologists, and orthopedic surgeons. Recently, many novel palliative treatment options have emerged for bone metastases, such as stereotactic body radiation therapy, radiopharmaceuticals, vertebroplasty, minimally invasive spine stabilization with percutaneous pedicle screws, acetabuloplasty, embolization, thermal ablation techniques, electrochemotherapy, and high-intensity focused ultrasound. These techniques are beneficial for patients who may not benefit from surgery or radiotherapy.

Availability and reporting quality of external validations of machine-learning prediction models with orthopedic surgical outcomes: a systematic review

Background and purpose - External validation of machine learning (ML) prediction models is an essential step before clinical application. We assessed the proportion, performance, and transparent reporting of externally validated ML prediction models in orthopedic surgery, using the Transparent Reporting for Individual Prognosis or Diagnosis (TRIPOD) guidelines.Material and methods - We performed a systematic search using synonyms for every orthopedic specialty, ML, and external validation. The proportion was determined by using 59 ML prediction models with only internal validation in orthopedic surgical outcome published up until June 18, 2020, previously identified by our group. Model performance was evaluated using discrimination, calibration, and decision-curve analysis. The TRIPOD guidelines assessed transparent reporting.Results - We included 18 studies externally validating 10 different ML prediction models of the 59 available ML models after screening 4,682 studies. All external validations identified in this review retained good discrimination. Other key performance measures were provided in only 3 studies, rendering overall performance evaluation difficult. The overall median TRIPOD completeness was 61% (IQR 43-89), with 6 items being reported in less than 4/18 of the studies.Interpretation - Most current predictive ML models are not externally validated. The 18 available external validation studies were characterized by incomplete reporting of performance measures, limiting a transparent examination of model performance. Further prospective studies are needed to validate or refute the myriad of predictive ML models in orthopedics while adhering to existing guidelines. This ensures clinicians can take full advantage of validated and clinically implementable ML decision tools.

Harrington rods for periacetabular pathological lesion: is it an option?

Advancement in cancer treatment has prolonged the survival of cancer patients; as a result, there are an increased number of patients with bone metastases and pathological fractures referred to orthopaedic surgeons for surgical intervention for a better quality of life. Metastasis around the hip joint can be painful and intervene with patients' daily activity, and reconstruction of the hip joint with periacetabular metastasis is complex and challenging especially longer cancer survivals might out-live their fixation. Several acetabular reconstruction techniques and implants have been described to overcome this problem; acetabular reconstruction and total hip arthroplasty still remains the standard surgical treatment, to relief pain and to improve function and quality of life. Harrington reconstruction of periacetabular metastatic disease combined with hip arthroplasty is one of the options that can address this clinical scenario safely; it is reproducible and cost-effective. In this review, we present case series of patients treated in our institution using Harrington rod technique for acetabular pathological lesions with good outcome.

Survival analysis after intramedullary stabilization for metastatic disease of the femur: prognostic value of common laboratory parameters

BACKGROUND

Metastatic disease of the femur confers significant morbidity and with improved survival due to advances in oncological treatment the prevalence in increasing. The aim of this study was to report on the outcomes of intramedullary stabilization for metastatic disease of the femoral shaft in a New Zealand centre and identify predictors of mortality.

METHODS

Ten-year retrospective review of clinical and radiographic records of patients treated with intramedullary stabilization for metastatic disease of the femur from a single tertiary referral hospital. Data on demographics, clinical and radiographic disease, laboratory markers, complications and mortality were collected. Univariate and multivariate analyses were used to determine predictors of mortality.

RESULTS

A total of 82 patients were reviewed (median age 72.5 years; 51% female). The most common primary tumour type was breast cancer (33%). Mortality rate was 15% and 77% at 30 days and 1 year, respectively. Multivariate analysis determined higher albumin (hazard ratio (HR) 0.51; P = 0.014) and higher Karnofsky Performance Score (HR 0.95; P < 0.001) were associated with reduced mortality risk; higher platelet count (HR 2.14; P = 0.009) and higher platelet : lymphocyte ratio (HR 1.87; P = 0.027) were associated with increased mortality risk.

CONCLUSIONS

Mortality rates were similar to those reported in other regions. Simple laboratory markers including serum albumin, platelet count and the platelet : lymphocyte ratio can aid clinicians in providing prognosis with surgical intervention.

Validation of PATHFx 2.0: An open-source tool for estimating survival in patients undergoing pathologic fracture fixation

Treatment decisions in patients with metastatic bone disease rely on accurate survival estimation. We developed the original PATHFx models using expensive, proprietary software and now seek to provide a more cost-effective solution. Using open-source machine learning software to create PATHFx version 2.0, we asked whether PATHFx 2.0 could be created using open-source methods and externally validated in two unique patient populations. The training set of a well-characterized, database records of 189 patients and the bnlearn package within R Version 3.5.1 (R Foundation for Statistical Computing), was used to establish a series of Bayesian belief network models designed to predict survival at 1, 3, 6, 12, 18, and 24 months. Each was externally validated in both a Scandinavian (n = 815 patients) and a Japanese (n = 261 patients) data set. Brier scores and receiver operating characteristic curves to assessed discriminatory ability. Decision curve analysis (DCA) evaluated whether models should be used clinically. DCA showed that the model should be used clinically at all time points in the Scandinavian data set. For the 1-month time point, DCA of the Japanese data set suggested to expect better outcomes assuming all patients will survive greater than 1 month. Brier scores for each curve demonstrate that the models are accurate at each time point. Statement of Clinical Significance: we successfully transitioned to PATHFx 2.0 using open-source software and externally validated it in two unique patient populations, which can be used as a cost-effective option to guide surgical decisions in patients with metastatic bone disease.

External Validation of PATHFx Version 3.0 in Patients Treated Surgically and Nonsurgically for Symptomatic Skeletal Metastases

BACKGROUND

PATHFx is a clinical decision-support tool based on machine learning capable of estimating the likelihood of survival after surgery for patients with skeletal metastases. The applicability of any machine-learning tool depends not only on successful external validation in unique patient populations but also on remaining relevant as more effective systemic treatments are introduced. With advancements in the treatment of metastatic disease, it is our responsibility to patients to ensure clinical support tools remain contemporary and accurate.

QUESTION/PURPOSES

Therefore, we sought to (1) generate updated PATHFx models using recent data from patients treated at one large, urban tertiary referral center and (2) externally validate the models using two contemporary patient populations treated either surgically or nonsurgically with external-beam radiotherapy alone for symptomatic skeletal metastases for symptomatic lesions.

METHODS

After obtaining institutional review board approval, we collected data on 208 patients undergoing surgical treatment for pathologic fractures at Memorial Sloan Kettering Cancer Center between 2015 and 2018. These data were combined with the original PATHFx training set (n = 189) to create the final training set (n = 397). We then created six Bayesian belief networks designed to estimate the likelihood of 1-month, 3-month, 6-month, 12-month, 18-month, and 24-month survival after treatment. Bayesian belief analysis is a statistical method that allows data-driven learning to arise from conditional probabilities by exploring relationships between variables to estimate the likelihood of an outcome using observed data. For external validation, we extracted the records of patients treated between 2016 and 2018 from the International Bone Metastasis Registry and records of patients treated nonoperatively with external-beam radiation therapy for symptomatic skeletal metastases from 2012 to 2016 using the Military Health System Data Repository (radiotherapy-only group). From each record, we collected the date of treatment, laboratory values at the time of treatment initiation, demographic data, details of diagnosis, and the date of death. All records reported sufficient follow-up to establish survival (yes/no) at 24-months after treatment. For external validation, we applied the data from each record to the new PATHFx models. We assessed calibration (calibration plots), accuracy (Brier score), discriminatory ability (area under the receiver operating characteristic curve [AUC]).

RESULTS

The updated PATHFx version 3.0 models successfully classified survival at each time interval in both external validation sets and demonstrated appropriate discriminatory ability and model calibration. The Bayesian models were reasonably calibrated to the Memorial Sloan Kettering Cancer Center training set. External validation with 197 records from the International Bone Metastasis Registry and 192 records from the Military Health System Data Repository for analysis found Brier scores that were all less than 0.20, with upper bounds of the 95% confidence intervals all less than 0.25, both for the radiotherapy-only and International Bone Metastasis Registry groups. Additionally, AUC estimates were all greater than 0.70, with lower bounds of the 95% CI all greater than 0.68, except for the 1-month radiotherapy-only group. To complete external validation, decision curve analysis demonstrated clinical utility. This means it was better to use the PATHFx models when compared to the default assumption that all or no patients would survive at all time periods except for the 1-month models. We believe the favorable Brier scores (< 0.20) as well as DCA indicate these models are suitable for clinical use.

CONCLUSIONS

We successfully updated PATHFx using contemporary data from patients undergoing either surgical or nonsurgical treatment for symptomatic skeletal metastases. These models have been incorporated for clinical use on PATHFx version 3.0 (https://www.pathfx.org). Clinically, external validation suggests it is better to use PATHFx version 3.0 for all time periods except when deciding whether to give radiotherapy to patients with the life expectancy of less than 1 month. This is partly because most patients survived 1-month after treatment. With the advancement of medical technology in treatment and diagnosis for patients with metastatic bone disease, part of our fiduciary responsibility is to the main current clinical support tools.

LEVEL OF EVIDENCE

Level III, therapeutic study.

Development and Internal Validation of Machine Learning Algorithms for Preoperative Survival Prediction of Extremity Metastatic Disease

BACKGROUND

A preoperative estimation of survival is critical for deciding on the operative management of metastatic bone disease of the extremities. Several tools have been developed for this purpose, but there is room for improvement. Machine learning is an increasingly popular and flexible method of prediction model building based on a data set. It raises some skepticism, however, because of the complex structure of these models.

QUESTIONS/PURPOSES

The purposes of this study were (1) to develop machine learning algorithms for 90-day and 1-year survival in patients who received surgical treatment for a bone metastasis of the extremity, and (2) to use these algorithms to identify those clinical factors (demographic, treatment related, or surgical) that are most closely associated with survival after surgery in these patients.

METHODS

All 1090 patients who underwent surgical treatment for a long-bone metastasis at two institutions between 1999 and 2017 were included in this retrospective study. The median age of the patients in the cohort was 63 years (interquartile range [IQR] 54 to 72 years), 56% of patients (610 of 1090) were female, and the median BMI was 27 kg/m (IQR 23 to 30 kg/m). The most affected location was the femur (70%), followed by the humerus (22%). The most common primary tumors were breast (24%) and lung (23%). Intramedullary nailing was the most commonly performed type of surgery (58%), followed by endoprosthetic reconstruction (22%), and plate screw fixation (14%). Missing data were imputed using the missForest methods. Features were selected by random forest algorithms, and five different models were developed on the training set (80% of the data): stochastic gradient boosting, random forest, support vector machine, neural network, and penalized logistic regression. These models were chosen as a result of their classification capability in binary datasets. Model performance was assessed on both the training set and the validation set (20% of the data) by discrimination, calibration, and overall performance.

RESULTS

We found no differences among the five models for discrimination, with an area under the curve ranging from 0.86 to 0.87. All models were well calibrated, with intercepts ranging from -0.03 to 0.08 and slopes ranging from 1.03 to 1.12. Brier scores ranged from 0.13 to 0.14. The stochastic gradient boosting model was chosen to be deployed as freely available web-based application and explanations on both a global and an individual level were provided. For 90-day survival, the three most important factors associated with poorer survivorship were lower albumin level, higher neutrophil-to-lymphocyte ratio, and rapid growth primary tumor. For 1-year survival, the three most important factors associated with poorer survivorship were lower albumin level, rapid growth primary tumor, and lower hemoglobin level.

CONCLUSIONS

Although the final models must be externally validated, the algorithms showed good performance on internal validation. The final models have been incorporated into a freely accessible web application that can be found at https://sorg-apps.shinyapps.io/extremitymetssurvival/. Pending external validation, clinicians may use this tool to predict survival for their individual patients to help in shared treatment decision making.

LEVEL OF EVIDENCE

Level III, therapeutic study.

Analysis of seven prognostic scores in patients with surgically treated epidural metastatic spine disease

BACKGROUND

Prognostic scores have been proposed to guide the treatment of patients with metastatic spine disease (MSD), but their accuracy and usefulness are controversial. The aim of this study was to evaluate seven such prognostic scoring systems. The following prognostic scores were compared: Tomita, Van der Linden (VDL), Bauer modified (BM), Oswestry Spinal Risk Index (OSRI), Tokuhashi original (T90), Tokuhashi revised (TR05), and modified Tokuhashi revised (TR17).

METHODS

We retrospectively reviewed all our patients who underwent surgery for spinal metastases, February 2008-January 2015. We classified all 223 patients into the predicted survival-time categories of each of the 7 scoring systems and then tallied how often this was correct vis-à-vis the actual survival time. Accuracy was also assessed using receiver operating characteristic (ROC) analysis at 1, 3, and 12 months.

RESULTS

The median (95% CI) survival of the 223 patients was 13.6 (7.9-19.3) months. A groupwise ROC analysis showed sufficient accuracy for 3-month survival only for TR17 (area under the curve [AUC] 0.71) and for 1-year survival for T90 (AUC 0.73), TR05 (AUC 0.76), TR17 (AUC 0.76), Tomita (AUC 0.77), and OSRI (AUC 0.71). A pointwise ROC score analysis showed poor prognostic ability for short-term survival (1 and 3 months) with sufficient accuracy for T90 (AUC 0.71), TR05 (AUC 0.71), TR17 (AUC 0.71), and the Tomita score (AUC 0.77) for 1-year survival.

CONCLUSION

The TR17 was the only prognostic system with acceptable performance here. More sophisticated assessment tools are required to keep up with present and future changes in tumor diagnostics and treatment.

Contemporary Management of Appendicular Skeletal Metastasis by Primary Tumor Type

Skeletal metastases exert a profound effect on patients and society, and will be encountered by most orthopedic surgeons. Once a primary malignancy is diagnosed, multidisciplinary management should focus on maximizing the quality of life while minimizing disease- and treatment-related morbidity. This may be best achieved with discerning attention to the unique characteristics of primary cancer types, including pathologic fracture healing rates, longevity, and efficacy of adjuvant therapies. Some lesions may respond well to nonsurgical measures, whereas others may require surgery. A single surgical intervention should allow immediate unrestricted activity and outlive the patient. In certain scenarios, a therapeutic benefit may be provided by excision with a curative intent. In these scenarios, or when endoprosthetic reconstruction is necessary, patients may be best referred to an orthopedic oncologist.

Prediction of survival after surgical management of femoral metastatic bone disease - A comparison of prognostic models

Background

Operative fixation for femoral metastatic bone disease is based on the principles of reducing pain and restoring function. Recent literature has proposed a number of prognostic models for appendicular metastatic bone disease. The aim of this study was to compare the accuracy of proposed soring systems in the setting of femoral metastatic bone disease in order to provide surgeons with information to determine the most appropriate scoring system in this setting.

Methods

A retrospective cohort analysis of patients who underwent surgical management of femoral metastatic bone disease at a single institution were included. A pre-operative predicted survival for all 114 patients was retrospectively calculated utilising the revised Katagiri model, PathFx model, SSG score, Janssen nomogram, OPTModel and SPRING 13 nomogram. Univariate and multivariate Cox regression proportional hazard models were constructed to assess the role of prognostic variables in the patient group. Area under the receiver characteristics and Brier scores were calculated for each prognostic model from comparison of predicted survival and actual survival of patients to quantify the accuracy of each model.

Results

For the femoral metastatic bone disease patients treated with surgical fixation, multivariate analysis demonstrated a number of pre-operative factors associated with survival in femoral metastatic bone disease, consistent with established literature. The OPTIModel demonstrated the highest accuracy at predicting 12-month (Area Under the Curve [AUC] = 0.79) and 24-month (AUC = 0.77) survival after surgical management. PathFx model was the most accurate at predicting 3-month survival (AUC = 0.70) and 6-month (AUC = 0.70) survival. The PathFx model was successfully externally validated in the femoral patient dataset for all time periods.

Conclusions

Among six prognostic models assessed in the setting of femoral metastatic bone disease, the present study observed the most accurate model for 3-month, 6-month, 12-month and 24-month survival. The results of this study may be utilised by the treating surgical team to determine the most accurate model for the required time period and therefore improve decision-making in the care of patients with femoral metastatic bone disease.

Predicting survival for metastatic spine disease: a comparison of nine scoring systems

BACKGROUND CONTEXT

Despite advances in spinal oncology, research in patient-based prognostic calculators for metastatic spine disease is lacking. Much of the literature in this area investigates the general predictive accuracy of scoring systems in heterogeneous populations, with few studies considering the accuracy of scoring systems based on patient specifics such as type of primary tumor.

PURPOSE

The aim of the present study was to compare the ability of widespread scoring systems to estimate both overall survival at various time points and tumor-specific survival for patients undergoing surgical treatment for metastatic spine disease in order to provide surgeons with information to determine the most appropriate scoring system for a specific patient and timeline.

STUDY DESIGN

This is a retrospective study.

PATIENT SAMPLE

Patients who underwent surgical resection for metastatic spine disease at a single institution were included.

OUTCOME MEASURES

Areas under the receiver operating characteristic curves were generated from comparison of actual survival of patients and survival as predicted by application of prevalent scoring systems.

METHODS

A preoperative score for all 176 patients was retrospectively calculated utilizing the Skeletal Oncology Research Group (SORG) Classic Scoring Algorithm, SORG Nomogram, original Tokuhashi, revised Tokuhashi, Tomita, original Bauer, modified Bauer, Katagiri, and van der Linden scoring systems. Univariate and multivariate Cox proportional hazard models were constructed to assess the association of patient variables with survival. Receiver operating characteristic analysis modeling was utilized to quantify the accuracy of each test at different end points and for different primary tumor subgroups. No funds were received in support of this work. The authors have no conflicts of interest to disclose.

RESULTS

Among all patients surgically treated for metastatic spine disease, the SORG Nomogram demonstrated the highest accuracy at predicting 30-day (area under the curve [AUC] 0.81) and 90-day (AUC 0.70) survival after surgery. The original Tokuhashi was the most accurate at predicting 365-day survival (AUC 0.78). Multivariate analysis demonstrated multiple preoperative factors strongly associated with survival after surgery for spinal metastasis. The accuracy of each scoring system in determining survival probability relative to primary tumor etiology and time elapsed since surgery was assessed.

CONCLUSIONS

Among the nine scoring systems assessed, the present study determined the most accurate scoring system for short-term (30-day), intermediate (90-day), and long-term (365-day) survival, relative to primary tumor etiology. The findings of the present study may be utilized by surgeons in a personalized effort to select the most appropriate scoring system for a given patient.

Diagnostic algorithm, prognostic factors and surgical treatment of metastatic cancer diseases of the long bones and spine

Oncological management of skeletal metastases has changed dramatically in the last few decades. A significant number of patients survive for many years with their metastases.

Surgeons are more active and the technical repertoire is broader, from plates to intramedullary devices to (tumour) endoprostheses.

The philosophy of treatment should be different in the case of a trauma-related fracture and a pathological fracture. A proper algorithm for establishing a diagnosis and evaluation of prognostic factors helps in planning the surgical intervention.

The aim of palliative surgery is usually to eliminate pain and to allow the patient to regain his/her mobility as well as to improve the quality of life through minimally invasive techniques using life-long durable devices.

In a selected group of patients with an oncologically controlled primary tumour site and a solitary bone metastasis with positive prognostic factors, which meet the criteria for radical excision (approximately 10% to 15% of the cases), a promising three to five years of survival may be achieved, especially in cases of metastases from breast and kidney cancer.

Spinal metastases require meticulous evaluation because decisions on treatment mostly depend on the tumour type, segmental stability, the patient’s symptoms and general state of health.

Advanced radiotherapy combined with minimally invasive surgical techniques (minimally invasive stabilisation and separation surgery) provides durable local control with a low complication rate in a number of patients.

Cite this article: EFORT Open Rev 2017;2:372-381.

Can A Multivariate Model for Survival Estimation in Skeletal Metastases (PATHFx) Be Externally Validated Using Japanese Patients?

BACKGROUND

Objective survival estimates are important when treating or studying outcomes in patients with skeletal metastases. One decision-support tool, PATHFx (www.pathfx.org) is designed to predict each patient's postsurgical survival trajectory at 1, 3, 6, and 12 months in patients undergoing stabilization for skeletal metastases. PATHFx has been externally validated in various western centers, but it is unknown whether it may be useful in Asian patient populations.

QUESTIONS/PURPOSES

We asked (1) whether the PATHFx models are as predictive in Japanese patients by estimating the area under the receiver operator characteristic curve (AUC); we considered an AUC greater than 0.7 as an adequate predictive value. We also (2) performed decision curve analysis at various times to determine whether and how PATHFx should be used clinically at those times.

PATIENTS AND METHODS

A Bayesian model is a statistical method to explore conditional, probabilistic relationships between variables to estimate the likelihood of an outcome using observed data. We applied the PATHFx Bayesian models to an independent dataset containing the records of patients who underwent skeletal stabilization for metastatic bone disease at one of five Japanese referral centers and had a followup longer than 12 months for survivors. Of 270 patients in the database, we excluded nine patients from analysis because their followup was less than 12 months, and finally we included 261 patients in the analysis. Data examined included age at the time of surgery, sex, indication for surgery (impending fracture or completed pathologic fracture), number of bone metastases (solitary or multiple), presence or absence of visceral or lymph node metastases, preoperative hemoglobin concentration, absolute lymphocyte count, and the primary oncologic diagnosis. We performed receiver operating characteristic curve analysis and estimated the AUC as a measure of discriminatory ability. Decision curve analysis was performed to determine if and how the models should be used in the clinical setting.

RESULTS

The AUCs for the 1-, 3-, 6-, and 12-month models were 0.77 (95% CI, 0.63-0.86), 0.80 (95% CI, 0.72-0.87), 0.83 (95% CI, 0.77-0.89), and 0.80 (95% CI, 0.75-0.86), respectively. Decision analysis indicated that the models conferred a positive net benefit (above the lines assuming none or all survive at each time) although the CIs of the AUC for 1 month were wide, suggesting that this dataset could not adequately predict 1-month survival.

CONCLUSIONS

Our findings show PATHFx is suitable for clinical use in Japan and may be used to guide surgical decision making or as a risk stratification method in support of clinical trials involving Japanese patients at 3, 6, and 12 months. More studies will be necessary to confirm the validity of the 1-month survival predictions of this mode. Other patient populations will need to be studied to confirm its usefulness in other non-Western and non-Japanese populations.

LEVEL OF EVIDENCE

Level II, prognostic study.

Can We Estimate Short- and Intermediate-term Survival in Patients Undergoing Surgery for Metastatic Bone Disease?

BACKGROUND

Objective means of estimating survival can be used to guide surgical decision-making and to risk-stratify patients for clinical trials. Although a free, online tool ( www.pathfx.org ) can estimate 3- and 12-month survival, recent work, including a survey of the Musculoskeletal Tumor Society, indicated that estimates at 1 and 6 months after surgery also would be helpful. Longer estimates help justify the need for more durable and expensive reconstructive options, and very short estimates could help identify those who will not survive 1 month and should not undergo surgery. Thereby, an important use of this tool would be to help avoid unsuccessful and expensive surgery during the last month of life.

QUESTIONS/PURPOSES

We seek to provide a reliable, objective means of estimating survival in patients with metastatic bone disease. After generating models to derive 1- and 6-month survival estimates, we determined suitability for clinical use by applying receiver operator characteristic (ROC) (area under the curve [AUC] > 0.7) and decision curve analysis (DCA), which determines whether using PATHFx can improve outcomes, but also discerns in which kinds of patients PATHFx should not be used.

METHODS

We used two, existing, skeletal metastasis registries chosen for their quality and availability. Data from Memorial Sloan-Kettering Cancer Center (training set, n = 189) was used to develop two Bayesian Belief Networks trained to estimate the likelihood of survival at 1 and 6 months after surgery. Next, data from eight major referral centers across Scandinavia (n = 815) served as the external validation set-that is, as a means to test model performance in a different patient population. The diversity of the data between the training set from Memorial Sloan-Kettering Cancer Center and the Scandinavian external validation set is important to help ensure the models are applicable to patients in various settings with differing demographics and treatment philosophies. We considered disease-specific, laboratory, and demographic information, and the surgeon's estimate of survival. For each model, we calculated the area under the ROC curve (AUC) as a metric of discriminatory ability and the Net Benefit using DCA to determine whether the models were suitable for clinical use.

RESULTS

On external validation, the AUC for the 1- and 6-month models were 0.76 (95% CI, 0.72-0.80) and 0.76 (95% CI, 0.73-0.79), respectively. The models conferred a positive net benefit on DCA, indicating each could be used rather than assume all patients or no patients would survive greater than 1 or 6 months, respectively.

CONCLUSIONS

Decision analysis confirms that the 1- and 6-month Bayesian models are suitable for clinical use.

CLINICAL RELEVANCE

These data support upgrading www.pathfx.org with the algorithms described above, which is designed to guide surgical decision-making, and function as a risk stratification method in support of clinical trials. This updating has been done, so now surgeons may use any web browser to generate survival estimates at 1, 3, 6, and 12 months after surgery, at no cost. Just as short estimates of survival help justify palliative therapy or less-invasive approaches to stabilization, more favorable survival estimates at 6 or 12 months are used to justify more durable, complicated, and expensive reconstructive options.

Prediction of survival after surgery due to skeletal metastases in the extremities

AIMS

The purpose of this study was to develop a prognostic model for predicting survival of patients undergoing surgery owing to metastatic bone disease (MBD) in the appendicular skeleton.

METHODS

We included a historical cohort of 130 consecutive patients (mean age 64 years, 30 to 85; 76 females/54 males) who underwent joint arthroplasty surgery (140 procedures) owing to MBD in the appendicular skeleton during the period between January 2003 and December 2008. Primary cancer, pre-operative haemoglobin, fracture versus impending fracture, Karnofsky score, visceral metastases, multiple bony metastases and American Society of Anaesthesiologist's score were included into a series of logistic regression models. The outcome was the survival status at three, six and 12 months respectively. Results were internally validated based on 1000 cross-validations and reported as time-dependent area under the receiver-operating characteristic curves (AUC) for predictions of outcome.

RESULTS

The predictive scores obtained showed AUC values of 79.1% (95% confidence intervals (CI) 65.6 to 89.6), 80.9% (95% CI 70.3 to 90.84) and 85.1% (95% CI 73.5 to 93.9) at three, six and 12 months.

DISCUSSION

In conclusion, we have presented and internally validated a model for predicting survival after surgery owing to MBD in the appendicular skeleton. The model is the first, to our knowledge, built solely on material from patients who only had surgery in the appendicular skeleton.

TAKE HOME MESSAGE

Applying this prognostic model will help determine whether the patients' anticipated survival makes it reasonable to subject them to extensive reconstructive surgery for which there may be an extended period of rehabilitation. Cite this article: Bone Joint J 2016;98-B:271-7.

2015 Marshall Urist Young Investigator Award: Prognostication in Patients With Long Bone Metastases: Does a Boosting Algorithm Improve Survival Estimates?

BACKGROUND

Survival estimation guides surgical decision-making in metastatic bone disease. Traditionally, classic scoring systems, such as the Bauer score, provide survival estimates based on a summary score of prognostic factors. Identification of new factors might improve the accuracy of these models. Additionally, the use of different algorithms--nomograms or boosting algorithms--could further improve accuracy of prognostication relative to classic scoring systems. A nomogram is an extension of a classic scoring system and generates a more-individualized survival probability based on a patient's set of characteristics using a figure. Boosting is a method that automatically trains to classify outcomes by applying classifiers (variables) in a sequential way and subsequently combines them. A boosting algorithm provides survival probabilities based on every possible combination of variables.

QUESTIONS/PURPOSES

We wished to (1) assess factors independently associated with decreased survival in patients with metastatic long bone fractures and (2) compare the accuracy of a classic scoring system, nomogram, and boosting algorithms in predicting 30-, 90-, and 365-day survival.

METHODS

We included all 927 patients in our retrospective study who underwent surgery for a metastatic long bone fracture at two institutions between January 1999 and December 2013. We included only the first procedure if patients underwent multiple surgical procedures or had more than one fracture. Median followup was 8 months (interquartile range, 3-25 months); 369 of 412 (90%) patients who where alive at 1 year were still in followup. Multivariable Cox regression analysis was used to identify clinical and laboratory factors independently associated with decreased survival. We created a classic scoring system, nomogram, and boosting algorithms based on identified variables. Accuracy of the algorithms was assessed using area under the curve analysis through fivefold cross validation.

RESULTS

The following factors were associated with a decreased likelihood of survival after surgical treatment of a metastatic long bone fracture, after controlling for relevant confounding variables: older age (hazard ratio [HR], 1.0; 95% CI, 1.0-1.0; p < 0.001), additional comorbidity (HR, 1.2; 95% CI, 1.0-1.4; p = 0.034), BMI less than 18.5 kg/m(2) (HR, 2.0; 95% CI, 1.2-3.5; p = 0.011), tumor type with poor prognosis (HR, 1.8; 95% CI, 1.6-2.2; p < 0.001), multiple bone metastases (HR, 1.3; 95% CI, 1.1-1.6; p = 0.008), visceral metastases (HR, 1.6; 95% CI, 1.4-1.9; p < 0.001), and lower hemoglobin level (HR, 0.91; 95% CI, 0.87-0.96; p < 0.001). The survival estimates by the nomogram were moderately accurate for predicting 30-day (area under the curve [AUC], 0.72), 90-day (AUC, 0.75), and 365-day (AUC, 0.73) survival and remained stable after correcting for optimism through fivefold cross validation. Boosting algorithms were better predictors of survival on the training datasets, but decreased to a performance level comparable to the nomogram when applied on testing datasets for 30-day (AUC, 0.69), 90-day (AUC, 0.75), and 365-day (AUC, 0.72) survival prediction. Performance of the classic scoring system was lowest for all prediction periods.

CONCLUSIONS

Comorbidity status and BMI are newly identified factors associated with decreased survival and should be taken into account when estimating survival. Performance of the boosting algorithms and nomogram were comparable on the testing datasets. However, the nomogram is easier to apply and therefore more useful to aid surgical decision making in clinical practice.

LEVEL OF EVIDENCE

Level III, prognostic study.

How do we estimate survival? External validation of a tool for survival estimation in patients with metastatic bone disease-decision analysis and comparison of three international patient populations

Background

We recently developed a clinical decision support tool, capable of estimating the likelihood of survival at 3 and 12 months following surgery for patients with operable skeletal metastases. After making it publicly available on www.PATHFx.org, we attempted to externally validate it using independent, international data.

Methods

We collected data from patients treated at 13 Italian orthopaedic oncology referral centers between 2010 and 2013, then applied to PATHFx, which generated a probability of survival at three and 12-months for each patient. We assessed accuracy using the area under the receiver-operating characteristic curve (AUC), clinical utility using Decision Curve Analysis (DCA), and compared the Italian patient data to the training set (United States) and first external validation set (Scandinavia).

Results

The Italian dataset contained 287 records with at least 12 months follow-up information. The AUCs for the three-month and 12-month estimates was 0.80 and 0.77, respectively. There were missing data, including the surgeon’s estimate of survival that was missing in the majority of records. Physiologically, Italian patients were similar to patients in the training and first validation sets. However notable differences were observed in the proportion of those surviving three and 12-months, suggesting differences in referral patterns and perhaps indications for surgery.

Conclusions

PATHFx was successfully validated in an Italian dataset containing missing data. This study demonstrates its broad applicability to European patients, even in centers with differing treatment philosophies from those previously studied.

Inaugural bone metastases in non-small cell lung cancer: a specific prognostic entity?

Background

In non-small cell lung cancer patients (NSCLC), median survival from the time patients develop bone metastasis is classically described being inferior to 6 months. We investigated the subcategory of patients having an inaugural skeletal-related-event revealing NSCLC. The purpose of this study was to assess the impact of bone involvement on overall survival and to determine biological and tumoral prognosis factors on OS and PFS. An analysis of the subgroup of solitary bone metastasis patients was also performed.

Methods

In a population of 1208 lung cancer patients, 55 consecutive NSCLC patients revealed by inaugural bone metastasis and treated between 2003 and 2010, were retrospectively analysed. Survival was measured with a Kaplan-Meyer curve. Univariate and multivariate analysis were performed using the Stepwise Cox proportional hazard regression model. A p value of less than 0,05 was considered statistically significant.

Results

Estimated incidence of revealing bone metastasis is 4,5% among newly diagnosed lung cancer patients. Median duration of skeletal symptoms before diagnosis was 3 months and revealing bone site was located on axial skeleton in 70% of the cases. Histology was adenocarcinoma (78%), with small primary tumors Tx-T1-2 accounting for 71% of patients. Rate of second SRE is 37%.

Median overall survival was 8.15 months, IQR [5–16 months], mean survival 13.4 months, and PFS was 3.5 months. In multivariate analysis, variables significantly associated with shortened survival were advanced T stage (HR = 2.8; p = 0.004), weight loss > 10% (HR = 3.1; p = 0.02), inaugural spinal epidural metastasis (HR 2.5; p = 0.0036), elevated C-reactive protein (HR = 4.3; p = 0.002) and TTF-1 status (HR = 2.42; p = 0.004). Inaugural spinal epidural metastasis is a very strong adverse pronostic factor in these cases, with a 3 months median survival. Single bone metastasis patients showed prolonged survival of 14.2 months versus 7.6 months, only in univariate analysis (HR = 0.42; p = 0.0059).

Conclusion

Prognosis of lung cancer patients with inaugural SRE remains pejorative. Accurately estimating the survival of this population is helpful for bone surgical decision-making at diagnosis. The trend for a higher proportion of adenocarcinoma in NSCLC patients should result with an increasing number of patients with inaugural SRE at diagnosis.

Skeletal metastases in 301 breast cancer patients: patient survival and complications after surgery

The aim was to identify prognostic variables associated with survival in 301 breast cancer patients after surgical treatment of skeletal metastases. The study period was 1986-2012. The median age at surgery was 61 (interquartile-range [IQR] 52-70) years. The cumulative 1-, 2-, and 5-year survival after surgery was 45% (95% CI 39-51), 27% (22-32), and 8% (5-12), respectively. The median follow-up time was 1 (IQR 0.2-2) year. Age over 60 years (Hazard ratio [HR] 1.9) and hemoglobin levels <110 g/L (HR 2) increased the risk of death after surgery. Patients with impending fractures (HR 0.4) had a lower death rate. The overall neurological function in patients with spinal metastases improved after surgery (p < 0.001). The complication rate was 25%, including 14% re-operations. Survival data and analysis of complications of this large cohort of surgically treated breast cancer patients help to set appropriate expectations for the patients, families, and medical staff.